The Lab

The SIPPRE Lab, located at the University of Peloponnese in the Patras Campus (K-building, 1st floor), is a cutting-edge research space dedicated to advancing the fields of Digital Signal Processing, Pattern Recognition, and Biomedical Signal Analysis. Equipped with state-of-the-art hardware and software, the lab is a hub for students and researchers to work on innovative projects that span areas such as brain-computer interfaces (BCI), medical image analysis, robotics, and game design.

At SIPPRE, we prioritize hands-on experimentation and open-source philosophy, enabling our students and researchers to tackle real-world problems and contribute to open scientific collaboration. Below, you’ll find our resources grouped into thematic areas with their relevance to ongoing research.

Brain-Computer Interfaces (BCI) and EEG Signal Analysis

We specialize in BCI applications and advanced EEG signal processing, supported by cutting-edge biosensing tools:

  • Cyton and Daisy EEG Boards (16 channels) and Ganglion Board (4 channels): Used for real-time brain signal acquisition in studies on emotion recognition, cognitive load, and neurofeedback-based BCI systems.
  • Enophones by Eno (EEG-integrated headphones): Combining EEG and audio functionality for immersive studies in cognitive performance and auditory processing.
  • EmotiBit Multisensor: Capturing physiological data such as heart rate, skin temperature, and emotional states, enabling multimodal analysis of human affective states.

Research projects include:

  • Emotion recognition through EEG signals for adaptive gaming.
  • Real-time BCI applications in entertainment and assistive technologies.
  • EEG signal analysis for sleep studies and health monitoring.

Medical Image Analysis and 3D Prototyping

Our lab contributes to medical imaging research by integrating advanced image processing techniques and machine learning:

  • NVIDIA 3080Ti (12GB): Powering deep learning models for cancer detection in mammograms, with ongoing projects utilizing GANs for breast density transformations.
  • Creality K1C 3D Printer: Prototyping custom tools and hardware accessories for experimental setups.

Software tools like MATLAB, OpenCV, and open-source frameworks (TensorFlow, PyTorch) are employed in:

  • Breast cancer detection through advanced image segmentation and classification.
  • Generative adversarial networks (GANs) for transforming and analyzing medical images.
  • Developing Computer-Assisted Diagnosis (CAD) systems.

Sound and Speech Processing

The lab is actively involved in audio analysis, focusing on emotion detection and speech enhancement:

  • Test Microphones for Acoustic Measurements: Essential for analyzing sound environments and speech signals in controlled and real-world conditions.

Software tools such as NVIDIA Riva, OpenSmile, and Librosa support:

  • Speech emotion recognition in therapeutic and educational applications.
  • Acoustic modeling for enhanced human-computer interaction systems.

Virtual Reality (VR) and Robotics

Our facilities support immersive applications and robotics, bridging the gap between cutting-edge technology and practical implementations:

  • Meta Quest 3 Headsets: Developing VR applications for BCI-enabled gaming and educational tools.
  • Tello Robomaster TT Drone: Exploring autonomous control using brain-computer interface signals and Python SDKs for robotics programming.

Applications include:

  • Brain-controlled drones for interactive demonstrations.
  • VR-based experiments for neurofeedback and motor imagery training.

Open-Source Development and Educational Prototypes

We actively promote open-source tools and student engagement:

  • Development Boards (Raspberry Pi 5, Arduino Uno R4): Enabling rapid prototyping in IoT, robotics, and educational experiments.
  • GitHub Repository: Showcasing our in-house software tools for medical image analysis, BCI systems, sound-speech processing, and student-led projects.

Examples include:

  • Student-designed platforms for emotion analysis in music and speech.
  • Algorithms for detecting abnormalities in medical imaging.

Software Resources

The SIPPRE Lab employs the latest software to support research across disciplines:

  • Deep Learning Frameworks: TensorFlow, Keras, PyTorch, and Python for scalable AI research.
  • Image Analysis: OpenCV for image recognition and analysis.
  • EEG Signal Processing: MNE tools and EEGLAB for robust brain signal analysis.
  • Sound and Speech Analysis: NVIDIA Riva, OpenSmile, and Librosa for audio feature extraction and analysis.

Visit Our GitHub Page

Our open-source projects and software tools are available on our GitHub page, including resources for:

  • Medical image analysis.
  • Brain-computer interfaces (BCI).
  • Sound and speech processing.
  • Student-led projects and experiments.